Introduction to PyTorch
Key concepts and skills covered
Basics of PyTorch, understanding what tensors are, and how to
perform basic operations on them.
- Usage of torch functions like empty, zeros, ones, and randn.
Understanding of automatic differentiation mechanism provided by
Understanding the concept of a Linear Layer in a neural network.
Building a simple linear regression model using PyTorch tensors and
Training a simple linear regression model, including defining a loss
function, an optimization function, and updating weights through
Understanding how to pass data in batches to a model during
Understanding basics of activation functions and how to use them in
Saving and loading PyTorch models, understanding the difference
between saving the whole model and just the state dictionary.
Understanding the concept of Transfer Learning, how to use
pretrained models in PyTorch.
Learning to modify a pre-trained model for a new task (fine-tuning).
Recognizing how to use different datasets and how to apply
transformations on them using torchvision.transforms.
Understanding how to evaluate a model by making predictions on test
Simplifying complex neural networks using nn.Sequential in PyTorch.
- Visualizing a neural network
- Visualizing PyTorch model, data and training with TensorBoard
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